Estimating plant nitrogen content in tomato using a smartphone

Optimizing nitrogen (N) fertilization is increasingly becoming a key issue to maximize productivity and farmers' income while reducing environmental impact of agricultural productions. Among the most sophisticated approaches to support variable rate N applications, a central role is played by frameworks that integrate satellite images and smart-scouting driven ground estimates of plant N content (PNC) and critical N concentration. Among the approaches to estimate PNC, the smartphone application PocketN demonstrated its suitability for cereals as well as its great integrability within digital platforms. In this study, we developed genotype-specific calibration curves to derive PNC of tomato crops from PocketN readings and we compared the performance of PocketN with the SPAD ones. Five commercial genotypes were grown in two field experiments in Northern and Southern Italy and four PocketN/SPAD readings and sampling events were carried out along the season. The most reliable relationships between PocketN/SPAD readings and PNC values from the laboratory were obtained for the readings carried out on the apical leaflet of the lower leaves of three plants. Mean R2 for all genotypes was 0.75 and 0.62 for PocketN and SPAD, respectively. This allows considering PocketN as a suitable tool for PNC estimates in light of its adoption within digital frameworks aimed at transferring precision agriculture principles to operational farming contexts.